A virtual environment for the design and simulated construction of prefabricated buildings

The construction industry has acknowledged that its current working practices are in need of substantial improvements in quality and efficiency and has identified that computer modelling techniques and the use of prefabricated components can help reduce times, costs, and minimise defects and problems of on-site construction. This paper describes a virtual environment to support the design and construction processes of buildings from prefabricated components and the simulation of their construction sequence according to a project schedule. The design environment can import a library of 3-D models of prefabricated modules that can be used to interactively design a building. Using Microsoft Project, the construction schedule of the designed building can be altered, with this information feeding back to the construction simulation environment. Within this environment the order of construction can be visualised using virtual machines. Novel aspects of the system are that it provides a single 3-D environment where the user can construct their design with minimal user interaction through automatic constraint recognition and view the real-time simulation of the construction process within the environment. This takes this area of research a step forward from other systems that only allow the planner to view the construction at certain stages, and do not provide an animated view of the construction process

Forensics in Industrial Control System: A Case Study

Industrial Control Systems (ICS) are used worldwide in critical infrastructures. An ICS system can be a single embedded system working stand-alone for controlling a simple process or ICS can also be a very complex Distributed Control System (DCS) connected to Supervisory Control And Data Acquisition (SCADA) system(s) in a nuclear power plant. Although ICS are widely used to-day, there are very little research on the forensic acquisition and analyze ICS artefacts. In this paper we present a case study of forensics in ICS where we de-scribe a method of safeguarding important volatile artefacts from an embedded industrial control system and several other source

Market Segmentation Trees

We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. The standard approach is to perform market segmentation by clustering users according to similarities in their contextual features, after which a "response model" is fit to each segment to model how users respond to personalized decisions. However, this methodology is not ideal for personalization, since two users could in theory have similar features but different response behaviors. We propose a general methodology, Market Segmentation Trees (MSTs), for learning interpretable market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs) which can be used to predict a user's choice amongst multiple options, and (ii) Isotonic Regression Trees (IRTs) which can be used to solve the bid landscape forecasting problem. We provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. We provide a theoretical analysis of the asymptotic running time of our training method validating its computational tractability on large datasets. We assess the practical performance of MSTs on several synthetic and real world datasets, showing our method reliably finds market segmentations which accurately model response behavior. Further, when applying MSTs to historical bidding data from a leading demand-side platform (DSP), we show that MSTs consistently achieve a 5-29% improvement in bid landscape forecasting accuracy over the DSP's current model. Our findings indicate that integrating market segmentation with response modeling consistently leads to improvements in response prediction accuracy, thereby aiding personalization

Market Segmentation Trees

Problem Definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. Methodology / Results: We propose a general methodology, Market Segmentation Trees (MSTs), for learning market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs), which can be used to predict a user’s choice amongst multiple options and (ii) Isotonic Regression Trees (IRTs), which can be used to solve the bid landscape forecasting problem. We provide a theoretical analysis of the asymptotic running times of our algorithmic methods, which validates their computational tractability on large datasets. We also provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. Finally, we assess the practical performance of MSTs on several synthetic and real world datasets, showing that our method reliably finds market segmentations which accurately model response behavior. Managerial Implications: The standard approach to conduct market segmentation for personalized decision-making is to first perform market segmentation by clustering users according to similarities in their contextual features, and then fit a “response model” to each segment in order to model how users respond to decisions. However, this approach may not be ideal if the contextual features prominent in distinguishing clusters are not key drivers of response behavior. Our approach addresses this issue by integrating market segmentation and response modeling, which consistently leads to improvements in response prediction accuracy, thereby aiding personalization. We find that such an integrated approach can be computationally tractable and effective even on large-scale datasets. Moreover, MSTs are interpretable since the market segments can easily be described by a decision tree and often require only a fraction of the number of market segments generated by traditional approaches

Superfluid to normal phase transition and extreme regularity of superdeformed bands

We derive the exact semiclassical expression for the second inertial parameter $\cal B$ for the superfluid and normal phases. Interpolation between these limiting values shows that the function ${\cal B}(I)$ changes sign at the spin $I_c$, which is critical for a rotational spectrum. The quantity $\cal B$ turns out to be a sensitive measure of the change in static pairing correlations. The superfluid-to-normal transition reveals itself in the specific variation of the ratio ${\cal B}/{\cal A}$ versus spin $I$ with the plateau characteristic of the normal phase. We find this dependence to be universal for normal deformed and superdeformed bands. The long plateau with a small value ${\cal B}/{\cal A}\sim A^{-8/3}$ explains the extreme regularity of superdeformed bands.Comment: 30 pages in LaTeX, 6 figures (PostScript). To be published in Yadernaya Fizika (Physics of Atomic Nuclei), special edition dedecated to the 90th birthday of Prof. I. I. Gurevit

Microscopic Study of Superdeformed Rotational Bands in 151Tb

Structure of eight superdeformed bands in the nucleus 151Tb is analyzed using the results of the Hartree-Fock and Woods-Saxon cranking approaches. It is demonstrated that far going similarities between the two approaches exist and predictions related to the structure of rotational bands calculated within the two models are nearly parallel. An interpretation scenario for the structure of the superdeformed bands is presented and predictions related to the exit spins are made. Small but systematic discrepancies between experiment and theory, analyzed in terms of the dynamical moments, J(2), are shown to exist. The pairing correlations taken into account by using the particle-number-projection technique are shown to increase the disagreement. Sources of these systematic discrepancies are discussed -- they are most likely related to the yet not optimal parametrization of the nuclear interactions used.Comment: 32 RevTeX pages, 15 figures included, submitted to Physical Review

Long-range Effects on the Pyroelectric Coefficient and Dielectric Susceptibility of a Ferroelectric Bilayer

Long-range effects on the pyroelectric coefficient and susceptibility of a ferroelectric bilayer with a ferroelectric interfacial coupling are investigated by use of the transverse Ising model within the framework of mean-field theory. The effects of the interfacial coupling and the transverse field on the pyroelectric coefficient and susceptibility of the bilayer are investigated by taking into account the long-range interaction. It is found that the pyroelectric coefficient and susceptibility increase with the decrease of the magnitude of the long-range interaction and the interfacial coupling when the temperature is lower than the phase transition temperature. We also find that the strong long-range interaction, the large transverse field and weak interfacial coupling can lead to the disappearance of some of the peaks of the pyroelectric coefficient and susceptibility of the ferroelectric bilayer. The phase transition temperature increases with the increase of the strength of the long-range interaction, which is similar to the results obtained in ferroelectric multi-layers or superlattice.Comment: 23 pages, 11 figure